Multidimensional Models in SQL Server Analysis Services

Multidimensional models are the traditional and most robust model type in SQL Server Analysis Services (SSAS). They are designed for complex business intelligence scenarios, offering powerful analytical capabilities and a rich querying experience.

Core Concepts of Multidimensional Models

Multidimensional models are built around the concept of a Cube. A cube is a multidimensional data structure that stores aggregated data, allowing for fast retrieval and analysis. The key components of a multidimensional model include:

Dimensions

Dimensions represent the perspectives or attributes by which you analyze data. Common examples include Time, Geography, Products, and Customers. Dimensions are typically hierarchical, allowing users to drill down or roll up data.

• Attributes: Members within a dimension (e.g., Year, Month, Day for a Time dimension).
• Hierarchies: Ordered sequences of attributes that define levels of analysis (e.g., a Year-Quarter-Month hierarchy).
• Attributes Relationships: Define how attributes are related within a dimension, improving query performance.

Measures

Measures represent the quantifiable data that you want to analyze. These are typically numeric values that can be aggregated, such as Sales Amount, Quantity, or Profit. Measures are stored in Measure Groups.

• Aggregation Functions: Specify how measures are aggregated (e.g., Sum, Count, Average, Min, Max).
• Calculated Measures: Measures derived from other measures or data, allowing for complex business logic to be expressed directly within the model.

Cubes

A cube is the central object in a multidimensional model. It is formed by combining one or more measure groups with associated dimensions. Cubes provide a unified view of data for analysis.

• Facts: The source data that measures are derived from, typically found in fact tables in a relational data warehouse.
• Schema: The underlying structure of the cube, defining the relationships between dimensions and measure groups.

Key Features and Benefits

• High Performance: Pre-aggregation of data and optimized storage structures ensure rapid query responses, even for very large datasets.
• Complex Calculations: Support for complex business logic through MDX (Multidimensional Expressions) for calculated measures and members.
• Rich User Experience: Facilitates intuitive exploration of data through drill-down, roll-up, slicing, and dicing operations.
• Scalability: Proven to scale to handle massive amounts of data and a large number of concurrent users.
• Broad Tool Support: Integrates seamlessly with various client tools like Excel, Power BI, and custom applications.

Designing a Multidimensional Model

Designing an effective multididimensional model involves several steps:

1. Understand Business Requirements: Identify the key questions users need to answer and the metrics they need to track.
2. Identify Dimensions and Measures: Determine the attributes for analysis and the quantifiable data points.
3. Design Dimensions: Create hierarchies and define attribute relationships.
4. Design Measure Groups: Define measures and their aggregation settings.
5. Build and Deploy the Cube: Use SQL Server Data Tools (SSDT) or Visual Studio with Analysis Services projects to design and deploy the model.
6. Process the Cube: Load data into the cube from the data source.

Working with Multidimensional Models

Once a cube is deployed and processed, users can interact with it using various client applications. The primary query language for multidimensional models is MDX (Multidimensional Expressions).

Example MDX Query

This MDX query retrieves the sum of 'Sales Amount' for the '2023' year, broken down by 'Product Category':

SELECT
    {[Measures].[Sales Amount]} ON COLUMNS,
    {[Product].[Category].[Category].Members} ON ROWS
FROM
    [YourCubeName]
WHERE
    ([Time].[Year].[2023])
            

Further Reading

• Design a Multidimensional Model
• MDX Syntax Elements
• Tabular Models vs. Multidimensional Models